@article{PAR00024289,
  title = {{I}maging an underwater basin and its resonance modes using optical fiber distributed acoustic sensing},
  author = {{L}ior, {I}. and {M}ercerat, {E}. {D}. and {R}ivet, {D}. and {S}laden, {A}. and {A}mpuero, {J}ean-{P}aul},
  editor = {},
  language = {{ENG}},
  abstract = {{D}istributed acoustic sensing ({DAS}) is an ideal tool for ambient noise tomography owing to the dense spatial measurements and the ability to continuously record in harsh environments, such as underwater. {A}lthough the fine spatial sampling of {DAS} facilitates the imaging of small-scale lateral velocity heterogeneities, efforts relying on dispersion based ambient noise tomography are hampered by the underlying premise of negligible lateral variations across the segment used for dispersion curve extraction. {T}o image small-scale structures, this method should be augmented with objective and independent approaches that are not scale limited. {H}ere, we show that power spectral densities ({PSD}s) and autocorrelations ({AC}s) of {DAS} data reveal extremely detailed frequency-dependent resonance and wave propagation characteristics. {T}hese observations contain crucial information on lateral and vertical wave propagation. {W}e use these methods to demonstrate the ability to image a complex underwater basin using ambient noise recorded on a fiber deployed offshore {G}reece. {A} 2{D} shear-wave velocity model was derived by analyzing {S}cholte-wave dispersion. {T}he {PSD} and {AC} reveal significant lateral variations across the short 2.5 km long fiber segment, including basin edge effects and scattered waves. {T}hese were used to further constrain and modify the velocity model. {T}he modified model is supported by waveform simulations that qualitatively reproduce the {PSD} and {AC} observations. {O}ur results demonstrate the advantages of incorporating {PSD} and {AC} observations into ambient noise-based imaging. {T}he spatially continuous observation of resonance modes across the basin highlights the benefit of {DAS} acquisitions for ground-motion estimation.},
  keywords = {{GRECE}},
  booktitle = {},
  journal = {{S}eismological {R}esearch {L}etters},
  volume = {93},
  numero = {3},
  pages = {1573--1584},
  ISSN = {0895-0695},
  year = {2022},
  DOI = {10.1785/0220210349},
  URL = {https://www.documentation.ird.fr/hor/{PAR}00024289},
}